{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "def loadtraindata():\n",
    "    path = r\"./train\"\n",
    "    trainset = torchvision.datasets.ImageFolder(path,\n",
    "                                                transform=transforms.Compose([\n",
    "                                                    transforms.Resize((100, 100)),  # 将图片缩放到指定大小（h,w）\n",
    "                                                    transforms.CenterCrop(64),\n",
    "                                                    transforms.ToTensor()])\n",
    "                                                )\n",
    "\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
    "                                              shuffle=True, num_workers=2)\n",
    "    return trainloader\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)  # 卷积层0\n",
    "        self.pool = nn.MaxPool2d(2, 2)  # 池化层\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)  # 卷积层\n",
    "        self.fc1 = nn.Linear(2704, 120)  # 全连接层\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 2704)\n",
    "\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "classes = ('不是奥特曼', '奥特曼')\n",
    "\n",
    "\n",
    "def loadtestdata():\n",
    "    path = r\"./test\"\n",
    "    testset = torchvision.datasets.ImageFolder(path,\n",
    "                                               transform=transforms.Compose([\n",
    "                                                   transforms.Resize((64, 64)),\n",
    "                                                   transforms.ToTensor()])\n",
    "                                               )\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=25,\n",
    "                                             shuffle=True, num_workers=2)\n",
    "    return testloader\n",
    "\n",
    "\n",
    "def matplotlib_imshow(img, one_channel=False):\n",
    "    if one_channel:\n",
    "        img = img.mean(dim=0)\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    if one_channel:\n",
    "        plt.imshow(npimg, cmap=\"Greys\")\n",
    "    else:\n",
    "        plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "        \n",
    "\n",
    "def images_to_probs(net, images):\n",
    "    output = net(images)\n",
    "    _, preds_tensor = torch.max(output, 1)\n",
    "    preds = np.squeeze(preds_tensor.numpy())\n",
    "    return preds, [F.softmax(el, dim=0)[i].item() for i, el in zip(preds, output)]\n",
    "\n",
    "\n",
    "def plot_classes_preds(net, images, labels):\n",
    "    preds, probs = images_to_probs(net, images)\n",
    "    fig = plt.figure(figsize=(12, 48))\n",
    "    for idx in np.arange(4):\n",
    "        ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])\n",
    "        matplotlib_imshow(images[idx], one_channel=True)\n",
    "        ax.set_title(\"{0}, {1:.1f}%\\n(label: {2})\".format(\n",
    "            classes[preds[idx]],\n",
    "            probs[idx] * 100.0,\n",
    "            classes[labels[idx]]),\n",
    "                    color=(\"green\" if preds[idx]==labels[idx].item() else \"red\"))\n",
    "    return fig\n",
    "\n",
    "\n",
    "def trainandsave():  # 训练\n",
    "    writer = SummaryWriter('runs/aoteman_1')\n",
    "    trainloader = loadtraindata()\n",
    "    net = Net()\n",
    "    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    for epoch in range(500):\n",
    "        running_loss = 0.0\n",
    "        for i, data in enumerate(trainloader, 0):\n",
    "            inputs, labels = data\n",
    "            inputs, labels = Variable(inputs), Variable(labels)\n",
    "\n",
    "            img_grid = torchvision.utils.make_grid(inputs)\n",
    "            writer.add_image('aoteman', img_grid)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = net(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            running_loss += loss.item()\n",
    "            if i % 40 == 39:\n",
    "                writer.add_scalar('training loss',running_loss / 40,epoch * len(trainloader) + i)\n",
    "                writer.add_figure('predictions vs. actuals',plot_classes_preds(net, inputs, labels),global_step=epoch * len(trainloader) + i)\n",
    "                print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 40))\n",
    "                running_loss = 0.0\n",
    "            torch.save(net, './out/net'+str(epoch)+'.pkl')\n",
    "            torch.save(net.state_dict(), './out/net_params'+str(epoch)+'.pkl')\n",
    "\n",
    "    print('Finished Training')\n",
    "\n",
    "\n",
    "\n",
    "def reload_net():\n",
    "    trainednet = torch.load('net.pkl')\n",
    "    return trainednet\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def test():\n",
    "    testloader = loadtestdata()\n",
    "    net = reload_net()\n",
    "    dataiter = iter(testloader)\n",
    "    images, labels = dataiter.next()\n",
    "    imshow(torchvision.utils.make_grid(images, nrow=5))\n",
    "    print('GroundTruth: '\n",
    "          , \" \".join('%5s' % classes[labels[j]] for j in range(25)))  # 打印前25个测试图\n",
    "    outputs = net(Variable(images))\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "    print('Predicted: ', \" \".join('%5s' % classes[predicted[j]] for j in range(25)))\n",
    "\n",
    "\n",
    "def test_one(img_path):\n",
    "    model = reload_net()\n",
    "    transform_valid = transforms.Compose([\n",
    "        transforms.Resize((64, 64), interpolation=2),\n",
    "        transforms.ToTensor()\n",
    "    ]\n",
    "    )\n",
    "    img = Image.open(img_path)\n",
    "    img_ = transform_valid(img).unsqueeze(0)\n",
    "    outputs = model(img_)\n",
    "    _, indices = torch.max(outputs, 1)\n",
    "    result = classes[indices]\n",
    "    print('predicted:', result)\n",
    "    print(outputs)\n",
    "\n",
    "\n",
    "def main():\n",
    "    trainandsave()\n",
    "    test()\n",
    "    test_one(r'./test1.jpg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26159 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22885 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29305 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26364 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26159 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22885 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29305 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26364 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[264,    40] loss: 0.004\n",
      "[265,    40] loss: 0.009\n",
      "[266,    40] loss: 0.004\n",
      "[267,    40] loss: 0.000\n",
      "[268,    40] loss: 0.010\n",
      "[269,    40] loss: 0.004\n",
      "[270,    40] loss: 0.004\n",
      "[271,    40] loss: 0.009\n",
      "[272,    40] loss: 0.010\n",
      "[273,    40] loss: 0.009\n",
      "[274,    40] loss: 0.004\n",
      "[275,    40] loss: 0.000\n",
      "[276,    40] loss: 0.004\n",
      "[277,    40] loss: 0.009\n",
      "[278,    40] loss: 0.004\n",
      "[279,    40] loss: 0.010\n",
      "[280,    40] loss: 0.004\n",
      "[281,    40] loss: 0.004\n",
      "[282,    40] loss: 0.010\n",
      "[283,    40] loss: 0.010\n",
      "[284,    40] loss: 0.005\n",
      "[285,    40] loss: 0.004\n",
      "[286,    40] loss: 0.004\n",
      "[287,    40] loss: 0.004\n",
      "[288,    40] loss: 0.004\n",
      "[289,    40] loss: 0.009\n",
      "[290,    40] loss: 0.010\n",
      "[291,    40] loss: 0.004\n",
      "[292,    40] loss: 0.009\n",
      "[293,    40] loss: 0.000\n",
      "[294,    40] loss: 0.005\n",
      "[295,    40] loss: 0.005\n",
      "[296,    40] loss: 0.010\n",
      "[297,    40] loss: 0.009\n",
      "[298,    40] loss: 0.004\n",
      "[299,    40] loss: 0.009\n",
      "[300,    40] loss: 0.005\n",
      "[301,    40] loss: 0.004\n",
      "[302,    40] loss: 0.009\n",
      "[303,    40] loss: 0.009\n",
      "[304,    40] loss: 0.005\n",
      "[305,    40] loss: 0.004\n",
      "[306,    40] loss: 0.004\n",
      "[307,    40] loss: 0.010\n",
      "[308,    40] loss: 0.004\n",
      "[309,    40] loss: 0.004\n",
      "[310,    40] loss: 0.010\n",
      "[311,    40] loss: 0.009\n",
      "[312,    40] loss: 0.000\n",
      "[313,    40] loss: 0.009\n",
      "[314,    40] loss: 0.005\n",
      "[315,    40] loss: 0.009\n",
      "[316,    40] loss: 0.004\n",
      "[317,    40] loss: 0.005\n",
      "[318,    40] loss: 0.009\n",
      "[319,    40] loss: 0.009\n",
      "[320,    40] loss: 0.009\n",
      "[321,    40] loss: 0.009\n",
      "[322,    40] loss: 0.005\n",
      "[323,    40] loss: 0.010\n",
      "[324,    40] loss: 0.010\n",
      "[325,    40] loss: 0.005\n",
      "[326,    40] loss: 0.009\n",
      "[327,    40] loss: 0.009\n",
      "[328,    40] loss: 0.004\n",
      "[329,    40] loss: 0.005\n",
      "[330,    40] loss: 0.009\n",
      "[331,    40] loss: 0.009\n",
      "[332,    40] loss: 0.005\n",
      "[333,    40] loss: 0.009\n",
      "[334,    40] loss: 0.009\n",
      "[335,    40] loss: 0.009\n",
      "[336,    40] loss: 0.004\n",
      "[337,    40] loss: 0.004\n",
      "[338,    40] loss: 0.000\n",
      "[339,    40] loss: 0.009\n",
      "[340,    40] loss: 0.005\n",
      "[341,    40] loss: 0.004\n",
      "[342,    40] loss: 0.009\n",
      "[343,    40] loss: 0.009\n",
      "[344,    40] loss: 0.000\n",
      "[345,    40] loss: 0.009\n",
      "[346,    40] loss: 0.010\n",
      "[347,    40] loss: 0.005\n",
      "[348,    40] loss: 0.009\n",
      "[349,    40] loss: 0.004\n",
      "[350,    40] loss: 0.009\n",
      "[351,    40] loss: 0.004\n",
      "[352,    40] loss: 0.004\n",
      "[353,    40] loss: 0.009\n",
      "[354,    40] loss: 0.004\n",
      "[355,    40] loss: 0.010\n",
      "[356,    40] loss: 0.009\n",
      "[357,    40] loss: 0.009\n",
      "[358,    40] loss: 0.009\n",
      "[359,    40] loss: 0.005\n",
      "[360,    40] loss: 0.009\n",
      "[361,    40] loss: 0.005\n",
      "[362,    40] loss: 0.005\n",
      "[363,    40] loss: 0.009\n",
      "[364,    40] loss: 0.005\n",
      "[365,    40] loss: 0.004\n",
      "[366,    40] loss: 0.005\n",
      "[367,    40] loss: 0.004\n",
      "[368,    40] loss: 0.009\n",
      "[369,    40] loss: 0.009\n",
      "[370,    40] loss: 0.009\n",
      "[371,    40] loss: 0.009\n",
      "[372,    40] loss: 0.009\n",
      "[373,    40] loss: 0.009\n",
      "[374,    40] loss: 0.005\n",
      "[375,    40] loss: 0.005\n",
      "[376,    40] loss: 0.009\n",
      "[377,    40] loss: 0.004\n",
      "[378,    40] loss: 0.005\n",
      "[379,    40] loss: 0.009\n",
      "[380,    40] loss: 0.004\n",
      "[381,    40] loss: 0.009\n",
      "[382,    40] loss: 0.004\n",
      "[383,    40] loss: 0.009\n",
      "[384,    40] loss: 0.004\n",
      "[385,    40] loss: 0.004\n",
      "[386,    40] loss: 0.009\n",
      "[387,    40] loss: 0.004\n",
      "[388,    40] loss: 0.009\n",
      "[389,    40] loss: 0.004\n",
      "[390,    40] loss: 0.005\n",
      "[391,    40] loss: 0.009\n",
      "[392,    40] loss: 0.009\n",
      "[393,    40] loss: 0.000\n",
      "[394,    40] loss: 0.009\n",
      "[395,    40] loss: 0.005\n",
      "[396,    40] loss: 0.005\n",
      "[397,    40] loss: 0.005\n",
      "[398,    40] loss: 0.009\n",
      "[399,    40] loss: 0.009\n",
      "[400,    40] loss: 0.005\n",
      "[401,    40] loss: 0.009\n",
      "[402,    40] loss: 0.009\n",
      "[403,    40] loss: 0.009\n",
      "[404,    40] loss: 0.000\n",
      "[405,    40] loss: 0.009\n",
      "[406,    40] loss: 0.009\n",
      "[407,    40] loss: 0.004\n",
      "[408,    40] loss: 0.009\n",
      "[409,    40] loss: 0.000\n",
      "[410,    40] loss: 0.005\n",
      "[411,    40] loss: 0.009\n",
      "[412,    40] loss: 0.000\n",
      "[413,    40] loss: 0.009\n",
      "[414,    40] loss: 0.009\n",
      "[415,    40] loss: 0.009\n",
      "[416,    40] loss: 0.004\n",
      "[417,    40] loss: 0.005\n",
      "[418,    40] loss: 0.004\n",
      "[419,    40] loss: 0.009\n",
      "[420,    40] loss: 0.009\n",
      "[421,    40] loss: 0.005\n",
      "[422,    40] loss: 0.009\n",
      "[423,    40] loss: 0.009\n",
      "[424,    40] loss: 0.005\n",
      "[425,    40] loss: 0.000\n",
      "[426,    40] loss: 0.009\n",
      "[427,    40] loss: 0.004\n",
      "[428,    40] loss: 0.000\n",
      "[429,    40] loss: 0.004\n",
      "[430,    40] loss: 0.004\n",
      "[431,    40] loss: 0.004\n",
      "[432,    40] loss: 0.005\n",
      "[433,    40] loss: 0.005\n",
      "[434,    40] loss: 0.004\n",
      "[435,    40] loss: 0.009\n",
      "[436,    40] loss: 0.004\n",
      "[437,    40] loss: 0.009\n",
      "[438,    40] loss: 0.009\n",
      "[439,    40] loss: 0.004\n",
      "[440,    40] loss: 0.009\n",
      "[441,    40] loss: 0.009\n",
      "[442,    40] loss: 0.004\n",
      "[443,    40] loss: 0.004\n",
      "[444,    40] loss: 0.004\n",
      "[445,    40] loss: 0.009\n",
      "[446,    40] loss: 0.004\n",
      "[447,    40] loss: 0.004\n",
      "[448,    40] loss: 0.005\n",
      "[449,    40] loss: 0.009\n",
      "[450,    40] loss: 0.005\n",
      "[451,    40] loss: 0.009\n",
      "[452,    40] loss: 0.005\n",
      "[453,    40] loss: 0.000\n",
      "[454,    40] loss: 0.004\n",
      "[455,    40] loss: 0.009\n",
      "[456,    40] loss: 0.000\n",
      "[457,    40] loss: 0.009\n",
      "[458,    40] loss: 0.000\n",
      "[459,    40] loss: 0.009\n",
      "[460,    40] loss: 0.009\n",
      "[461,    40] loss: 0.009\n",
      "[462,    40] loss: 0.004\n",
      "[463,    40] loss: 0.009\n",
      "[464,    40] loss: 0.009\n",
      "[465,    40] loss: 0.005\n",
      "[466,    40] loss: 0.009\n",
      "[467,    40] loss: 0.004\n",
      "[468,    40] loss: 0.000\n",
      "[469,    40] loss: 0.005\n",
      "[470,    40] loss: 0.009\n",
      "[471,    40] loss: 0.009\n",
      "[472,    40] loss: 0.009\n",
      "[473,    40] loss: 0.004\n",
      "[474,    40] loss: 0.000\n",
      "[475,    40] loss: 0.004\n",
      "[476,    40] loss: 0.005\n",
      "[477,    40] loss: 0.004\n",
      "[478,    40] loss: 0.005\n",
      "[479,    40] loss: 0.004\n",
      "[480,    40] loss: 0.005\n",
      "[481,    40] loss: 0.009\n",
      "[482,    40] loss: 0.009\n",
      "[483,    40] loss: 0.004\n",
      "[484,    40] loss: 0.005\n",
      "[485,    40] loss: 0.005\n",
      "[486,    40] loss: 0.009\n",
      "[487,    40] loss: 0.005\n",
      "[488,    40] loss: 0.005\n",
      "[489,    40] loss: 0.000\n",
      "[490,    40] loss: 0.005\n",
      "[491,    40] loss: 0.009\n",
      "[492,    40] loss: 0.009\n",
      "[493,    40] loss: 0.000\n",
      "[494,    40] loss: 0.004\n",
      "[495,    40] loss: 0.000\n",
      "[496,    40] loss: 0.000\n",
      "[497,    40] loss: 0.004\n",
      "[498,    40] loss: 0.005\n",
      "[499,    40] loss: 0.004\n",
      "[500,    40] loss: 0.009\n",
      "Finished Training\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:  不是奥特曼   奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼   奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼   奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼   奥特曼 不是奥特曼 不是奥特曼 不是奥特曼   奥特曼   奥特曼 不是奥特曼\n",
      "Predicted:  不是奥特曼   奥特曼 不是奥特曼 不是奥特曼   奥特曼 不是奥特曼   奥特曼 不是奥特曼   奥特曼   奥特曼 不是奥特曼   奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼 不是奥特曼   奥特曼 不是奥特曼 不是奥特曼   奥特曼 不是奥特曼 不是奥特曼\n",
      "predicted: 奥特曼\n",
      "tensor([[-0.1119,  0.0654]], grad_fn=<AddmmBackward>)\n"
     ]
    }
   ],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def matplotlib_imshow(img, one_channel=False):\n",
    "    if one_channel:\n",
    "        img = img.mean(dim=0)\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    if one_channel:\n",
    "        plt.imshow(npimg, cmap=\"Greys\")\n",
    "    else:\n",
    "        plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "writer = SummaryWriter('runs/fashion_mnist_experiment_1')\n",
    "dataiter = iter(loadtraindata())\n",
    "images, labels = dataiter.next()\n",
    "img_grid = torchvision.utils.make_grid(images)\n",
    "matplotlib_imshow(img_grid, one_channel=True)\n",
    "writer.add_image('four_fashion_mnist_images', img_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer.add_graph(Net(), images)\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
